Problem: time series prediction
The nonlinear autoregressive exogenous model: The Nonlinear autoregressive exogenous (NARX) model, which predicts the current value of a time series based upon its previous values as well as the current and past values of multiple driving (exogenous) series.
However, few NARX models can capture the long-term temporal dependencies appropriately and select the relevant driving series to make a prediction.
2 issues:
1. capture the long-term temporal dependencies
2. select the relevant driving series to make a prediction
We propose a dual-stage attention-based RNN to address these 2 issues.
1. first stage: input attention mechanism to extract relevant driving series.
2. second stage: temporal attention mechanism.
attention-based encoder-decoder networks for time series prediction/ LSTM/ GRU
One problem with encoder-decoder networks is that their performance will deteriorate rapidly as the length of input sequence increases.
Contribution: the two-stage attention mechanism. input attention for driving series and temporal attention for all time stamps.
input attention can select the relevant driving series.
temporal attention capture temporal information.
Supplementary knowledge:
1. what is driving series?
PP: A dual-stage attention-based recurrent neural network for time series prediction
原文：https://www.cnblogs.com/dulun/p/12267003.html